Radar Target Detection on Matrix Manifolds with Optimal Geometric Measure Selection

Matrix information geometry (MIG) detectors have demonstrated advantages for radar target detection in heterogeneous clutter. However, existing MIG methods often select a geometric measure empirically and perform detection without adjusting for the natural fluctuation of the clutter, which poses a limitation under dynamically varying strong clutter. To address these limitations, this paper proposes a normalized geometric measure ratio-based matrix information geometry (NGMR-MIG) framework with optimal geometric measure selection from commonly used candidates for radar target detection. Representative divergence-type and distance-type measures are first organized into a candidate pool and then evaluated by the normalized geometric measure ratio (NGMR) to determine the optimal one. The NGMR is defined as the ratio of the deviation between the cell under test (CUT) and the clutter centroid to the average intrinsic dispersion of the reference cells. A larger NGMR therefore means the CUT stands out from the clutter background, implying the presence of a target. Experiments on simulated and measured radar datasets show that NGMR-MIG improves detection performance by 3–5 dB over classical MIG detectors in heterogeneous environments.